Method and system for decoding encoded images and reducing the size of said images

ABSTRACT

A method and system for reducing the number of mathematical operations required in the JPEG decoding process without substantially impacting the quality of the image displayed is disclosed. Embodiments provide an efficient JPEG decoding process for the purposes of displaying an image on a display smaller than the source image, for example, the screen of a handheld device. According to one aspect of the invention, this is accomplished by reducing the amount of processing required for dequantization and inverse DCT (IDCT) by effectively reducing the size of the image in the quantized, DCT domain prior to dequantization and IDCT. This can be done, for example, by discarding unnecessary DCT index rows and columns prior to dequantization and IDCT. In one embodiment, columns from the right, and rows from the bottom are discarded such that only the top left portion of the block of quantized, and DCT coefficients are processed.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. Ser. No. 13/278,489filed Oct. 21, 2011, which is a continuation of U.S. Ser. No.12/038,905, filed Feb. 2, 2008, both of which are expressly incorporatedherein by reference in their entirety.

FIELD OF THE INVENTION

The present invention relates generally to digital image processing.More particularly, the present invention relates to a method fordecreasing the computing power required to decompress JPEG-encodeddigital images for the purpose of displaying them on small screens suchas those found on mobile handheld devices.

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by any one of the patentdocument or patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightswhatsoever.

BACKGROUND OF THE INVENTION

Digital imaging has, in recent years, become the leading standard bywhich photographs, designs and other visual elements are captured,created, stored and displayed. Images, like all computer-readable media,contain a significant amount of binary data which employs a significantamount of computing resources, particularly in terms of storagerequirements. As a result, compression algorithms and decoders are usedto make image information fit into a smaller data file.

Compression algorithms vary widely depending on the nature of the datain question and are well known in the art. In terms of digital imaging,one such algorithm is the JPEG, or Joint Photographic Experts Groupcompression, as defined in Terminal Equipment and Protocols forTelematic Services: Information Technology—Digital Compression andCoding of Continuous-Tone Still Images—Requirements and Guidelines,CCITT Rec. T.81 (1992 E), which is herein incorporated by reference inits entirety. According to this method, an image file, which is made upof a large matrix of spatial coefficients corresponding to pixelinformation, is first transformed from the pixel domain to a compresseddomain using a mathematical transform. The transform typically employed,as will be appreciated by one skilled in the art, is the Discrete CosineTransform or DCT. This transform is particularly effective for imagecompression since it stores the majority of the original signal data inlow-frequency components in the transformed, DCT domain. The resultantcoefficients in the frequency domain are then quantized, or reduced insize, by factors that depend on the coefficients' relative importance tothe overall integrity of the image. This quantization process is a“lossy” compression as some of the image data will be lost as atrade-off for saving space and computing resources. The frequencycomponents of the image that will be most noticeable to the human eyewill naturally be quantized to a lower degree, as losses in theinformation in this frequency range will be more damaging to the overallintegrity of the image.

Following quantization, the image file is a matrix of quantized DCTcoefficients, also known as quantized DCT indices, and is substantiallysmaller than the uncompressed binary data that made up the raw image. Asa final step, this matrix goes through a process known as entropyencoding to further reduce it in size. Many entropy encoding proceduresare known in the art. One such example is Huffman encoding. For thepurposes of the following discussion, a person skilled in the art willappreciate that any reference to Huffman decoding can equally refer toany other method of entropy decoding. Once the Huffman encoding processis complete, the resultant file is a JPEG encoded image which can bestored within any kind of computer-readable storage means. JPEGcompression is well known in the art, and those of ordinary skill in thefield will appreciate that varying techniques in carrying out theprocess may be used.

In order for the compressed image to be viewed or edited, the compressedimage file must go through the reverse of the encoding process. Ingeneral, the stored JPEG image first goes through Huffman decodingfollowed by dequantization, or up-scaling each of the frequencycomponents of the image by the same factor as they were downscaledduring compression, and finally, the inverse of the DCT operation. Inthis manner, the stored JPEG image can then be displayed.

Like the compression process, the decompression requires significantcomputing power, as many calculations are necessary in order to applyeach step of the algorithm. In cases where limited computing power isavailable, this process can be detrimental to overall system performanceand can result in long lag times. This is especially true for mobilehandheld devices such as cellphones, PDAs and the like which, by virtueof their size, have reduced computing resources.

There is an additional problem that renders JPEG decompression difficultfor handheld devices. Since they almost invariably possess screens whichare smaller in size (and/or with lower resolution) than the image whichthey seek to display, an un-downsampled or spatially un-downscaled imagewill typically not fit within the confines of the screen. As such, theimage must be spatially downscaled, for example, by taking the averageof adjacent pixel data or truncating a portion of the pixel coefficientsfrom the image file. Regardless of which method is employed, however,additional calculations must be performed, in order to carry out thespatial downscaling. This can result in increased delay times in loadingan image for viewing on a mobile, handheld device. In some cases,important image data is discarded as a sacrifice for faster imageprocessing. This can result in distortion of the image.

It is, therefore, desirable to provide a method to quickly decompressand view JPEG-encoded images using fewer computing resources, whileensuring that the image displayed is as faithful as possible to thesource image.

BRIEF DESCRIPTION OF THE DRAWINGS

Other aspects and features of the present invention will become apparentto those ordinarily skilled in the art upon review of the followingdescription of specific embodiments of the invention in conjunction withthe accompanying figures. Embodiments of the present invention will nowbe described, by way of example only, with reference to the attachedFigures, wherein:

FIG. 1 is a block diagram of the standard JPEG decompression anddownsampling process.

FIG. 2 shows the matrices involved in the decompression and downsamplingprocess.

FIG. 3 is a block diagram of the decompression and downsampling methodin accordance with an embodiment of the present invention.

FIG. 4 a shows the general truncation operation performed on a DCT indexblock in accordance with an embodiment of the present invention, whileFIG. 4 b shows an exemplary version of the same operation.

FIG. 5 shows the downsampling and dequantization process for one DCTindex block of a JPEG-encoded image according to one embodiment of theinvention, namely, for cases where a JPEG-encoded image is to be reducedby a factor of 1.6.

FIG. 6 shows the steps involved in decoding one DCT index block of aJPEG-encoded image according to an embodiment of the invention, wherebya JPEG encoded image is to be reduced by a factor of 4.

FIG. 7 shows the steps involved in JPEG decoding and resizing a JPEGimage according to one embodiment of the present invention.

FIG. 8 is a block diagram of an exemplary embodiment of a mobile devicewhich includes an image module capable of carrying out the methodsdescribed herein.

DETAILED DESCRIPTION

It is an object to obviate or mitigate at least one disadvantage ofprevious JPEG decoders, and e.g. to reduce the long load times requiredto display JPEG-encoded images on screens smaller than the resolution ofthe uncompressed image.

We have developed a method and system for reducing the number ofmathematical operations required in the JPEG decoding process withoutsubstantially impacting the quality of the image displayed. Embodimentsprovide an efficient JPEG decoding process for the purposes ofdisplaying an image on a display smaller than the source image, forexample, the screen of a handheld device.

According to one aspect of the invention, this is accomplished byreducing the amount of processing required for dequantization andinverse DCT (IDCT) by effectively reducing the size of the image in thequantized, DCT domain prior to dequantization and IDCT. This can bedone, for example, by discarding unnecessary DCT index rows and columnsprior to dequantization and IDCT. In one embodiment, columns from theright, and rows from the bottom are discarded such that only the topleft portion of the block of quantized, and DCT coefficients areprocessed. Another embodiment utilizes the fast AAN IDCT transform,named after the authors Y. Arai, T. Agui, and M. Nakajima, “A fastDCT-SQ scheme for images,” Trans. IEICE, vol. E-71, no. 11, pp.1095-1097, November 1988, the contents of which are hereby incorporatedby reference in their entirety. In this embodiment, rows and columns arediscarded which are not needed for an AAN IDCT transform to produce adownscaled image using mean filtering.

Accordingly, one aspect of the invention provides a method of decodingan encoded image, the method comprising: performing an entropy decodingoperation on a compressed image to generate an image file made up ofindex blocks in a quantized, transform domain; reducing the image sizein the quantized, transform domain to generate an image file made up ofreduced index blocks in the quantized, transform domain prior todequantization and inverse transform operations; dequantizing thereduced index blocks by multiplying each block by a quantization stepsize block to generate an image file made up of quantized coefficientblocks in the dequantized, transform domain; and performing an inversetransform operation to generate an image file of reduced pixel valueblocks in the pixel domain. Such a method is particularly well suitedfor producing an image file which is reduced in size from that of anoriginal JPEG encoded image. This is advantageous for presentation on adisplay smaller in size than size of the original JPEG encoded image,and is particularly advantageous for reducing the processing required todo so as compared to prior art methods. Another aspect of the inventionprovides a device for decoding and displaying an encoded image,comprising: means for performing an entropy decoding operation on acompressed image to generate an image file made up of index blocks in aquantized, transform domain; means for reducing the image size in thequantized, transform domain to generate an image file made up of reducedindex blocks in the quantized, transform domain prior to dequantizationand inverse transform operations; means for dequantizing the reducedindex blocks by multiplying each block by a quantization step size blockto generate an image file made up of quantized coefficient blocks in thedequantized, transform domain; means performing an inverse transformoperation to generate an image file of reduced pixel value blocks in thepixel domain; and display means for displaying said image file. Anotheraspect of the invention provides a computer program product stored in amachine readable medium comprising instructions, which when executed bya processor of a device, causes said device to decode a JPEG-encodedimage, said instructions comprising instructions for: performing anentropy decoding operation on a compressed JPEG image to generate animage file made up of index blocks in a quantized, transform domain;reducing the image size in the quantized, transform domain to generatean image file made up of reduced index blocks in the quantized,transform domain prior to dequantization and inverse transformoperations; dequantizing the reduced index blocks by multiplying eachblock by a quantization step size block to generate an image file madeup of quantized coefficient blocks in the dequantized, transform domain;and performing an inverse transform operation to generate an image fileof reduced pixel value blocks in the pixel domain.

FIG. 1 shows the block diagram of the standard JPEG decompression anddownsampling process. The process begins with a compressed JPEG image100 stored in, for example, a flash memory device. The first operationis Huffman decoding 101, whereby a JPEG-encoded image is reduced to aset of 8×8 blocks of quantized DCT indices.

Following Huffman decoding, the blocks must be dequantized.Dequantization 102 is essentially the inverse of the quantizationoperation. Each frequency component is multiplied by the scale factor bywhich it was divided during compression in order to regenerate theoriginal dequantized frequency components.

The components must be transformed back to the pixel-domain in order toreconstruct the image, because one of the steps in the JPEG compressionprocess is the application of a Discrete Cosine Transform (DCT) to mapthe original source image from the pixel domain to the DCT domain. AnInverse Discrete Cosine Transform (IDCT) 103 thus forms the next step inthe decompression process. The IDCT operation is well-known in the art.

The IDCT operation results in the full, uncompressed JPEG image in thepixel-domain 104. In order to display the image on a screen of aspecific size, particularly that of a mobile, handheld device, the imagemust be spatially downscaled 105 to fit the resolution of the screen.Once this is done, the smaller image 106 may be displayed.

FIG. 2 shows the matrices behind each of the steps described above.Following Huffman decoding, there is a full image block 201 which ismade up of DCT index blocks 202. Each DCT index block 202 is an 8×8matrix of quantized DCT indices 203. As each of these indices must bedequantized by a corresponding quantization step size, thedequantization operation 102 is best accomplished by multiplying eachDCT index block 202 by a corresponding quantization step size matrix204. For example, a DCT index block of size 8×8 will be multiplied by aquantization step size matrix also of size 8×8. This is a scalarmultiplication, whereby each element in the DCT index block 202 ismultiplied by its corresponding element in the quantization step sizematrix 204. Since a DCT index block is 8×8, this dequantizationoperation requires a total of 64 multiplications per block.

The completion of the dequantization operation results in an imagematrix made up of blocks of dequantized DCT indices 205. As these blockscontain coefficients in the DCT domain, they must be converted back tothe pixel domain to reconstruct the original image. This is accomplishedby applying an Inverse DCT (IDCT) to each element of the dequantized DCTindex blocks. The IDCT operation is well known in the art, as it hasapplications across many forms of signal processing, including digitalimaging. The DCT and IDCT operations are discussed at length by by N.Ahmed, T. Natarajan, and K. R. Rao, “Discrete Cosine Transform”, IEEETrans. Computers, 90-93, January 1974, which is incorporated herein byreference.

For an 8×8 block of DCT coefficients f_(nm), the 2D IDCT algorithm isgiven by

$x_{ij} = {\frac{1}{4}{\sum\limits_{n = 0}^{7}{\sum\limits_{m = 0}^{7}{\cos\;\frac{\pi\; n\left( {{2i} + 1} \right)}{16}\cos\;\frac{\pi\left( {{2j} + 1} \right)}{16}a_{n}a_{m}f_{n\; m}}}}}$where x_(ij) is the image coefficient in the spatial domain at theposition of (i, j). The coefficient a_(n) is given by

$a_{0} = \frac{1}{\sqrt{2}}$and a_(n)=1 for 1≦n≦7. This operation can be seen as a cascade of 1Dtransforms applied to the rows and columns of the 8×8 block of DCTcoefficients. The 1D transform is given by

$x_{k} = {\sum\limits_{n = 0}^{N - 1}{\cos\;\frac{\pi\;{n\left( {{2k} + 1} \right)}}{16}a_{n}y_{n}}}$where x_(k) is the original source signal and y_(n) is its correspondingDCT coefficient. The coefficient a_(n) is given by

$a_{0} = {{\frac{1}{\sqrt{8}}\mspace{14mu}{and}\mspace{14mu} a_{n}} = \sqrt{\frac{2}{8}}}$for 1≦n≦7.

Since the IDCT operation is carried out as a matrix multiplication, thetwo-dimensional IDCT operation can be represented as

$X = {\frac{1}{8}C^{T}{FC}}$where F is an 8×8 block of dequantized DCT coefficients, X is itscorresponding block of image coefficients 206 in the pixel domain, C isa matrix defined as:

$C = \begin{bmatrix}1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\{\sqrt{2}\cos\;\frac{1}{16}\pi} & {\sqrt{2}\cos\;\frac{3}{16}\pi} & {\sqrt{2}\cos\;\frac{5}{16}\pi} & {\sqrt{2}\cos\;\frac{7}{16}\pi} & {\sqrt{2}\cos\;\frac{9}{16}\pi} & {\sqrt{2}\cos\frac{11}{16}\pi} & {\sqrt{2}\cos\;\frac{13}{16}\pi} & {\sqrt{2}\cos\;\frac{15}{16}\pi} \\{\sqrt{2}\cos\;\frac{2}{16}\pi} & {\sqrt{2}\cos\;\frac{6}{16}\pi} & {\sqrt{2}\cos\;\frac{10}{16}\pi} & {\sqrt{2}\cos\;\frac{14}{16}\pi} & {\sqrt{2}\cos\;\frac{18}{16}\pi} & {\sqrt{2}\cos\;\frac{22}{16}} & {\sqrt{2}\cos\;\frac{26}{16}\pi} & {\sqrt{2}\cos\;\frac{30}{16}\pi} \\{\sqrt{2}\cos\;\frac{3}{16}\pi} & {\sqrt{2\;}\cos\;\frac{9}{16}\pi} & {\sqrt{2}\cos\;\frac{15}{16}\pi} & {\sqrt{2}\cos\;\frac{21}{16}\pi} & {\sqrt{2}\cos\;\frac{27}{16}\pi} & {\sqrt{2}\cos\;\frac{33}{16}\pi} & {\sqrt{2}\cos\;\frac{39}{16}\pi} & {\sqrt{2}\cos\;\frac{45}{16}\pi} \\{\sqrt{2}\cos\;\frac{4}{16}\pi} & {\sqrt{2}\cos\;\frac{12}{16}\pi} & {\sqrt{2}\cos\;\frac{20}{16}\pi} & {\sqrt{2}\cos\;\frac{28}{16}\pi} & {\sqrt{2}\cos\;\frac{36}{16}\pi} & {\sqrt{2}\cos\;\frac{44}{16}\pi} & {\sqrt{2}\cos\;\frac{52}{16}\pi} & {\sqrt{2}\cos\;\frac{60}{16}\pi} \\{\sqrt{2}\cos\;\frac{5}{16}\pi} & {\sqrt{2}\cos\;\frac{15}{16}\pi} & {\sqrt{2}\cos\;\frac{25}{16}\pi} & {\sqrt{2}\cos\;\frac{35}{16}\pi} & {\sqrt{2}\cos\;\frac{45}{16}\pi} & {\sqrt{2}\cos\;\frac{55}{16}\pi} & {\sqrt{2}\cos\;\frac{65}{16}\pi} & {\sqrt{2}\cos\;\frac{75}{16}\pi} \\{\sqrt{2}\cos\;\frac{6}{16}\pi} & {\sqrt{2}\cos\;\frac{18}{16}\pi} & {\sqrt{2}\cos\;\frac{30}{16}\pi} & {\sqrt{2}\cos\;\frac{42}{16}\pi} & {\sqrt{2}\cos\;\frac{54}{16}\pi} & {\sqrt{2}\cos\;\frac{66}{16}\pi} & {\sqrt{2}\cos\;\frac{78}{16}\pi} & {\sqrt{2}\cos\;\frac{90}{16}\pi} \\{\sqrt{2}\cos\;\frac{7}{16}\pi} & {\sqrt{2}\cos\;\frac{21}{16}\pi} & {\sqrt{2}\cos\;\frac{35}{16}\pi} & {\sqrt{2}\cos\;\frac{49}{16}\pi} & {\sqrt{2}\cos\;\frac{63}{16}\pi} & {\sqrt{2}\cos\;\frac{77}{16}\pi} & {\sqrt{2}\cos\;\frac{91}{16}\pi} & {\sqrt{2}\cos\;\frac{105}{16}\pi}\end{bmatrix}$and C^(T) is its transpose. In order to arrive at a block of imagecoefficients 206, 4,096 multiplications must be carried out perdequantized DCT index block in addition to the 64 multiplicationsrequired for the dequantization operation. As a result, a computer mustcarry out 4,160 multiplications in order to convert each Huffman-decodedDCT index block 202 back to its original block of image coefficients 206in the pixel domain. Since a full image comprises a very large number ofDCT index blocks, the dequantization and IDCT operation often requiressignificant computing power. Once the IDCT operations are complete, theresulting matrix is the original, uncompressed image 207.

There is one final operation that must be carried out before the imagecan be displayed on a small screen. An image with a resolution of1280×1024, for example, will only fit on a screen of 320×256 pixels ifthe image is downscaled by a factor of 4. This imparts an additionalmathematical step to the overall display operation. The image must bespatially scaled, or downsampled by a factor determined by the originalimage size relative to the size of the screen on which it is to bedisplayed. Needless to say, the computational complexity in carrying outall of the foregoing operations is substantial.

One example of a method for reducing the complexity inherent in thedecompression and downscaling operations involves performing thedownsampling operation in the DCT domain by discarding higher-order DCTindices following the dequantization calculation. This has the advantageof reducing the complexity of the IDCT operation, as the size of theblocks which undergo the IDCT operation has been reduced. An example ofsuch a method is discussed in U.S. Pat. No. 7,050,656 to Bhaskaran, etal., which is incorporated herein by reference. However, downsampling inthe dequantized DCT domain still requires the dequantization operationfor every quantized DCT index.

A decoder according to an embodiment of the present invention reducescomputational complexity even further by reducing the number ofcalculations needed for each of the dequantization and IDCT operations.This is achieved by reducing the size of the Image block made ofquantized DCT index blocks in the quantized DCT index domain, bydiscarding rows and columns of the quantized DCT index blocks. Thisreduces the number of multiplications required during dequantization andfor the IDCT operation, as only a smaller block of DCT indices needundergo the dequantization and IDCT operations.

FIG. 3 shows a block diagram of the fast JPEG decoder in accordance withan embodiment of the present invention. A JPEG image undergoes Huffmandecoding 302, similar to the previous methods. This decoder, however,takes the full image block 303 which is made up of quantized DCT indexblocks and reduces the size of the image block made of quantized DCTindex blocks in the quantized DCT index domain 306 for example, bydiscarding the rows and columns of the quantized DCT index blocks. Thisresults in an image block made of quantized DCT index blocks withreduced rows and columns 308. As should be appreciated, correspondingrows and columns in the quantization matrix are also discardable, priorto the dequantization step 310. This results in the image block made ofdequantized DCT index blocks with reduced rows and columns 312. Thisdownscaled image block then undergoes an IDCT transform 314 to producethe image made of downscaled image blocks 320. That should beappreciated the IDCT transform 314 can comprise separate row and columnIDCT transforms.

We will now discuss a method of discarding rows and columns in step 306,according to an embodiment of the invention. Referring to FIG. 4 a,Huffman decoding results in an image matrix 401 made up of quantized DCTindex blocks 402. Rather than dequantizing every element of each indexblock 402, a decoder according to one embodiment of the presentinvention first reduces the index block size from the standard 8×8 to anN×N sized block 404, where 8/N is the factor, S, by which an image is tobe downscaled in order to fit the desired screen size. N may be anyinteger, as long as it is less than 8, but not less than 1. Thisreduction is accomplished by truncating the higher-order quantized DCTindices from each DCT index block 402. By cutting off all indices ofhigher order from all of the index blocks, the whole image matrix 401,when reduced, contains substantially less data and therefore requiresfewer calculations in the decompression operation. A person skilled inthe art will appreciate that the higher order DCT indices correspond tothe coefficients for higher frequency values. Since the nature of theDCT index blocks 402 is such that lower order, and therefore lowerfrequency, DCT indices are concentrated in the upper-left hand corner ofeach DCT index block 402, truncating indices except for the upperleft-hand corner of the matrix will cut off the higher order indiceswhile retaining the lower order ones. This can be accomplished withoutsubstantially damaging the integrity of the image, since variations inthe strength of brightness at high frequencies are less discernable bythe human eye than variations at lower frequencies.

FIG. 4 b illustrates this operation by way of example. Returning to theexample proposed earlier, suppose an image of 1280×1024 is to bedisplayed on a screen with a resolution of 320×256. The image is to bereduced by a factor of S=4; therefore, N would be equal to 2, and each8×8 quantized DCT index block 402 would be reduced to a size 2×2 matrix406. With the matrix of this size, the full dequantization step sizematrix 407 (as stored in the JPEG header) is no longer required, and itcan be truncated to include only the step sizes that correspond to thelower-order indices retained in the 2×2 matrix 406. The dequantizationoperation would now only require 4 multiplications rather than the 64that would be required by the decompression methods taught by the priorart. Further, now that the image data has been reduced in accordancewith the desired display size, the downsampling calculation 105 in thespatial domain is no longer necessary, as the downsampling effectivelyoccurs in step 306 in this embodiment.

N need not be limited to 2. One of skill in the art will appreciate thatN could be any number, subject to the constraint 1≦N≦7.

Computing complexity is further reduced by the truncation operationsince the device need only apply an IDCT operation to the newly-reducedDCT index blocks. In many cases, this operation can be furthersimplified. FIG. 5 illustrates this advantage of a decoder according toan embodiment of the present invention by way of example. Suppose, forthe purposes of this example, N=5. First, all quantized DCT indices oforder higher than 5 are discarded from the DCT index block F_(ij) 501resulting in reduced DCT index block F_(ij)′ 502. Then, the reducedblock is dequantized through scalar multiplication of reduced DCT indexblock 502 by reduced quantization step table 503. Followingdequantization 504, the reduced, dequantized DCT index block 505undergoes an IDCT operation. Based on the matrix form of the IDCTdiscussed above, the value of C for a 5×5 IDCT is given by

$c = \begin{bmatrix}1 & 1 & 1 & 1 & 1 \\{\sqrt{2}\cos\;\frac{1}{10}\pi} & {\sqrt{2}\cos\;\frac{3}{10}\pi} & {\sqrt{2}\cos\;\frac{5}{10}\pi} & {\sqrt{2}\cos\;\frac{7}{10}\pi} & {\sqrt{2}\cos\;\frac{9}{10}\pi} \\{\sqrt{2}\cos\;\frac{2}{10}\pi} & {\sqrt{2}\cos\;\frac{6}{10}\pi} & {\sqrt{2}\cos\;\frac{10}{10}\pi} & {\sqrt{2}\cos\;\frac{14}{10}\pi} & {\sqrt{2}\cos\;\frac{18}{10}} \\{\sqrt{2}\cos\;\frac{3}{10}\pi} & {\sqrt{2\;}\cos\;\frac{9}{10}\pi} & {\sqrt{2}\cos\;\frac{15}{10}\pi} & {\sqrt{2}\cos\;\frac{21}{10}\pi} & {\sqrt{2}\cos\;\frac{27}{10}\pi} \\{\sqrt{2}\cos\;\frac{4}{10}\pi} & {\sqrt{2}\cos\;\frac{12}{10}\pi} & {\sqrt{2}\cos\;\frac{20}{10}\pi} & {\sqrt{2}\cos\;\frac{28}{10}\pi} & {\sqrt{2}\cos\;\frac{36}{10}\pi}\end{bmatrix}$By using the substitution

${{\sqrt{2}\cos\;\frac{k\;\pi}{10}} = c_{k}},$for k=1, 2, 3, 4, this matrix can be represented as

$C = \begin{bmatrix}1 & 1 & 1 & 1 & 1 \\c_{1} & c_{3} & 0 & {- c_{3}} & {- c_{1}} \\c_{2} & {- c_{4}} & {- \sqrt{2}} & {- c_{4}} & c_{2} \\c_{3} & {- c_{1}} & 0 & c_{1} & {- c_{3}} \\c_{4} & {- c_{2}} & \sqrt{2} & {- c_{2}} & c_{4}\end{bmatrix}$Through basic matrix factorization, this matrix can be simplified to

$C = {{\begin{bmatrix}1 & 0 & 0 & 0 & 0 \\0 & 0 & 0 & 1 & 0 \\0 & 1 & 0 & 0 & 0 \\0 & 0 & 0 & 0 & 1 \\0 & 0 & 1 & 0 & 0\end{bmatrix}\begin{bmatrix}1 & 1 & 1 & 0 & 0 \\c_{2} & {- c_{4}} & {- \sqrt{2}} & 0 & 0 \\c_{4} & {- c_{2}} & \sqrt{2} & 0 & 0 \\0 & 0 & 0 & {- c_{3}} & {- c_{1}} \\0 & 0 & 0 & c_{1} & {- c_{3}}\end{bmatrix}}\begin{bmatrix}1 & 0 & 0 & 0 & 1 \\0 & 1 & 0 & 1 & 0 \\0 & 0 & 1 & 0 & 0 \\0 & {- 1} & 0 & 1 & 0 \\{- 1} & 0 & 0 & 0 & 1\end{bmatrix}}$The IDCT transform can then be expressed as

${\begin{bmatrix}y_{0} & y_{1} & y_{2} & y_{3} & y_{4}\end{bmatrix}C} = {\begin{bmatrix}{y_{0} + {c_{2}y_{2}} + {c_{4}y_{4}}} \\{y_{0} - {c_{4}y_{2}} - {c_{2}y_{4}}} \\{y_{0} - {\sqrt{2}y_{2}} + {\sqrt{2}y_{4}}} \\{{{- c_{3}}y_{1}} + {c_{1}y_{3}}} \\{{{- c_{1}}y_{1}} - {c_{3}y_{3}}}\end{bmatrix}^{T}\begin{bmatrix}1 & 0 & 0 & 0 & 1 \\0 & 1 & 0 & 1 & 0 \\0 & 0 & 1 & 0 & 0 \\0 & {- 1} & 0 & 1 & 0 \\{- 1} & 0 & 0 & 0 & 1\end{bmatrix}}$Where y_(k) is the DCT coefficient for 0≦k≦4, and T means transposition.The above transform can be further simplified using the followingequations:y ₀ +c ₂ y ₂ +c ₄ y ₄ =y ₀+(c ₂ −c ₄)(y ₂ −y ₄)/2+(c ₂ +c ₄)(y ₂ +y ₄)/2y ₀ −c ₄ y ₂ −c ₂ y ₄ =y ₀+(c ₂ −c ₄)(y ₂ −y ₄)/2−(c ₂ +c ₄)(y ₂ +y ₄)/2y ₀−√{square root over (2)}y ₂+√{square root over (2)}y ₄ =y ₀−2(c ₂ −c₄)(y ₂ −y ₄)c ₃ y ₁ −c ₁ y ₃ =c ₃(y ₁ +y ₃)−(c ₁ +c ₃)y ₃c ₁ y ₁ +c ₃ y ₃ =c ₃(y ₁ +y ₃)+(c ₁ −c ₃)y ₁As can be seen by comparing the left and right sides of the above 5equations, the un-simplified (left side of the) equations require 10multiplications, while the simplified (right side of the) equations onlyneed 5 multiplications. Therefore, only 5 multiplications are requiredfor each column or row IDCT operation. As there are 5 rows and 5columns, a total of 50 multiplications are required for the inversetransform. Dequantization requires 25 additional multiplications. As aresult, in this example, a total of only 75 multiplication operationsare required for the downsampling, dequantization and IDCT calculationsfor each 8×8 DCT index block for cases where N=5. As can be seen, thisis a significant improvement over the multiplications needed in aconventional JPEG decompression, even when Fast IDCT algorithms (e.g.,AAN) are used. For example, we believe the AAN algorithm proposed in Y.Arai, T. Agui, and M. Nakajima, “A fast DCT-SQ scheme for images,”Trans. IEICE, vol. E-71, no. 11, pp. 1095-1097, November 1988. (thecontents of which are hereby incorporated by reference), would require144 multiplications for this example.

In a further example, consider the application for values where N=2. Asshown in FIG. 6, a Huffman-decoded DCT index block 601 is truncated to areduced, 2×2 sized block 602. The quantization step table is alsotruncated to size 2×2 603, and as a result, the dequantization operation604 requires only 4 multiplations to arrive at the reduced, dequantizedDCT index block 605. For this case, the matrix C for carrying out theIDCT calculation is given by

$C = \begin{bmatrix}1 & 1 \\1 & {- 1}\end{bmatrix}$Thus, the IDCT operation 606 for cases where N=2 becomes greatlysimplified, and many of the multiplication operations required forhigher values of N are no longer necessary.

Furthermore, the blocks need not be confined to square matrices. Bysubstituting the N×N matrix for one of size H×V, an image can be scaledto fit a rectangular screen of virtually any dimension, as long as H andV are both integers that are less than 8 but greater than or equal to 1.

FIG. 7 shows the steps involved in decoding and resizing a JPEG imageaccording to one embodiment of the present invention. A JPEG-encodedimage file 701 first undergoes Huffman decoding 702, which results in animage file made up of 8×8 quantized DCT index blocks 703. Values ofvariables H and V are then chosen 710 based on the vertical andhorizontal factors, S, respectively by which the image is to bedownscaled. For example, if the image is to be reduced by a factor of 2in both length and width, both H and V will be 4, according to theformula

${S = \frac{8}{N}},$where N is either H or V. H and V need not be the same if the image isto be downscaled a different amount in the horizontal dimension from thevertical dimension. The DCT indices of orders higher than H and V arethen discarded 704 from each 8×8 DCT index block, resulting in an imagefile made up of H×V quantized DCT index blocks 705. Each H×V quantizedDCT index block is then dequantized 706 through multiplication by aquantization step size block of size H×V. This results in an image filemade up of dequantized DCT index blocks 707. Finally, the H×V matrixform of the IDCT operation 708 is performed on each H×V dequantized DCTindex block, which results in an image file made up of H×V imagecoefficient blocks 709 in the pixel domain.

Some embodiments of the invention can implement an optional featurewhich provides an additional advantage over the prior art. Since much ofeach quantized DCT index block is discarded, a full Huffman decoding ofeach block is no longer necessary. As one of reasonable skill in the artwill appreciate, one of the stages of Huffman decoding is an amplitudedetermination, or, in terms of the JPEG standard cited above (CCITT Rec.T.81 (1992 E)), the “Extend procedure.” This step is not necessary forany indices that are to be truncated prior to dequantization, sincetheir individual amplitudes will have no bearing on the reconstructedimage. As a result, the amplitude determination step of the Huffmandecoding may be limited to the lower-order coefficients which will beretained during the downsampling operation. In other words, according toan embodiment, we skip the Extend (ZZ(K, Size)) step (e.g., as shown inFIG. F.14 on page 107 of the above referenced (CCITT Rec. T.81 (1992 E))for each position K which falls outside the truncated area (e.g., is notwithin the truncated matrix F_(ij)′).

We will now describe an alternative embodiment, which utilizes adifferent method for determining which rows and columns to discard instep 306.

Downscaling can also be realized with mean filtering. Table 1illustrates an example of downscaling an image in the spatial domain bya factor of 4 in both horizontal and vertical axes. In this example the8×8 block is divided into four 4×4 blocks. Each of the 4×4 blocks arethen replaced with a single spatial co-efficient equal to the average ofthe pixels within the 4×4 block as illustrated in Table 1.

TABLE 1 Downscaling by mean filtering $\left. \begin{bmatrix}x_{00} & x_{01} & x_{02} & x_{03} & x_{04} & x_{05} & x_{06} & x_{07} \\x_{10} & x_{11} & x_{12} & x_{13} & x_{14} & x_{15} & x_{16} & x_{17} \\x_{20} & x_{21} & x_{22} & x_{23} & x_{24} & x_{25} & x_{26} & x_{27} \\x_{30} & x_{31} & x_{32} & x_{33} & x_{34} & x_{35} & x_{36} & x_{37} \\x_{40} & x_{41} & x_{42} & x_{43} & x_{44} & x_{45} & x_{46} & x_{47} \\x_{50} & x_{51} & x_{52} & x_{53} & x_{54} & x_{55} & x_{56} & x_{57} \\x_{60} & x_{61} & x_{62} & x_{63} & x_{64} & x_{65} & x_{66} & x_{67} \\x_{70} & x_{71} & x_{72} & x_{73} & x_{74} & x_{75} & x_{76} & x_{77}\end{bmatrix}\rightarrow\begin{bmatrix}y_{00} & y_{01} \\y_{10} & y_{11}\end{bmatrix} \right.$In FIG. 1, x_(i,j)'s, 0≦i≦7, 0≦j≦7 are spatial domain coefficients,y_(i,j)'s, 0≦i≦1, 0≦j≦1 are downscaled coefficients. The downscalingoperation can be expressed as

${y_{00} = {\frac{1}{16}{\sum\limits_{i = 0}^{i = 3}{\sum\limits_{j = 0}^{j = 3}x_{ij}}}}},{y_{01} = {\frac{1}{16}{\sum\limits_{i = 0}^{i = 3}{\sum\limits_{j = 4}^{j = 4}x_{ij}}}}},{y_{10} = {\frac{1}{16}{\sum\limits_{i = 4}^{i = 7}{\sum\limits_{j = 0}^{j = 3}x_{ij}}}}},{y_{11} = {\frac{1}{16}{\sum\limits_{i = 4}^{i = 7}{\sum\limits_{j = 4}^{j = 7}{x_{ij}.}}}}}$The above operation can be looked as a cascade of 1-dimensional meanfilters for rows and columns. For each 1-dimensional case (i.e, for eachrow and column), the down sampling operation can be expressed by thefollowing transform into the frequency domain:

$\begin{matrix}{y_{0} = {\frac{x_{0} + x_{1} + x_{2} + x_{3}}{4} = {\frac{1}{4}{\sum\limits_{n = 0}^{3}{\sum\limits_{k = 0}^{7}{\cos\;\frac{\pi\; n\left( {{2k} + 1} \right)}{16}a_{n}f_{n}}}}}}} & (1) \\{y_{1} = {\frac{x_{4} + x_{5} + x_{6} + x_{7}}{4} = {\frac{1}{4}{\sum\limits_{n = 4}^{7}{\sum\limits_{k = 0}^{7}{\cos\;\frac{\pi\;{n\left( {{2k} + 1} \right)}}{16}a_{n}f_{n}}}}}}} & (2)\end{matrix}$Where f_(n) is the DCT coefficient, 0≦n≦7, x_(j) is the spatial domaincoefficient, 0≦j≦7, and y_(k) is the downsampled coefficient, 0≦k≦1,a₀=1/√{square root over (8)}, and a_(n)=√{square root over (2)}/√{squareroot over (8)} for 1≦n≦7.

Following the similar procedure as AAN algorithm for 8×8 DCT in [1], theabove two equations can be simplified by merging a so-called weightingmatrix into the quantization table to produce the following:y ₀ =f ₀+(f ₁ −f ₇)×2.6132593+(f ₅ −f ₃)×1.0823922  (3)y ₁ =f ₀−(f ₁ −f ₇)×2.6132593−(f ₅ −f ₃)×1.0823922  (4)Assume the column IDCT transform is first applied. From the equations(3) and (4), we can see that for each row or column, only thecoefficients f_(i), i=0, 1, 3, 5, 7, are required for the computation.That is, we only need to dequantize DCT indices within rows 0, 1, 3, 5and 7 and columns 0, 1, 3, 5 and 7. In other words, the DCT indices inrows and columns 2, 4 and 6 are not used. Thus these rows and columnscan be discarded in step 306 as they do no affect the result. Therefore,only 25 multiplications are required for dequantization instead of 64multiplications for a full inverse quantization with respect to an 8×8DCT block. The two coefficients 2.6132593 and 1.0823933 in equations (3)and (4) can be merged into the quantization table.

Furthermore, as DCT indices within rows and columns 2, 4 and 6 of an 8×8DCT block are not required for the computation, there is no need toactually produce them. Accordingly, an embodiment of the inventionsimplifies the entropy decoding operation by eliminating steps of theentropy decoding which would produce the non-zero DCT indices of theindex blocks to be discarded. For example, an embodiment of theinvention further reduces processing by decreasing the amount ofprocessing utilized for Huffman decoding, in a manner similar to thatdescribed above. For example, according to an embodiment, the Extendprocedure, which forms part of the Huffman decoding process, is notexecuted for rows and columns which will be discarded. Accordingly, suchan embodiment reduces the processing requirements for Huffman decodingby eliminating the need for Huffman decoding operations for the“non-zero DCT indices” in those discarded rows and columns.

Another aspect of the invention provides a computer program productstored in a machine readable medium comprising instructions, which, whenexecuted by a processor of a device, causes said device to carry out themethods described herein.

We will discuss the methods and systems with reference to a particularexemplary application for which the embodiments of the invention arewell suited, namely a wireless network where image files are compressedfor transmission to a mobile wireless communication device, hereafterreferred to as a mobile device. Examples of applicable communicationdevices include pagers, cellular phones, cellular smart-phones, wirelessorganizers, personal digital assistants, computers, laptops, handheldwireless communication devices, wirelessly enabled notebook computersand the like.

The mobile device is a two-way communication device with advanced datacommunication capabilities including the capability to communicate withother mobile devices or computer systems through a network oftransceiver stations. The mobile device may also have the capability toallow voice communication. Depending on the functionality provided bythe mobile device, it may be referred to as a data messaging device, atwo-way pager, a cellular telephone with data messaging capabilities, awireless Internet appliance, or a data communication device (with orwithout telephony capabilities). To aid the reader in understanding thestructure of the mobile device and how it communicates with otherdevices and host systems, reference will now be made to FIG. 8.

FIG. 8 is a block diagram of an exemplary embodiment of a mobile device100. The mobile device 100 includes a number of components such as amain processor 182 that controls the overall operation of the mobiledevice 100. Communication functions, including data and voicecommunications, are performed through a communication subsystem 184.Data received by the mobile device 100 can be decompressed and decryptedby decoder 183, operating according to any suitable decompressiontechniques (e.g. YK decompression, and other known techniques) andencryption techniques (e.g. using an encryption techniques such as DataEncryption Standard (DES), Triple DES, or Advanced Encryption Standard(AES)). The communication subsystem 184 receives messages from and sendsmessages to a wireless network 200. In this exemplary embodiment of themobile device 100, the communication subsystem 104 is configured inaccordance with the Global System for Mobile Communication (GSM) andGeneral Packet Radio Services (GPRS) standards. The GSM/GPRS wirelessnetwork is used worldwide and it is expected that these standards willbe superseded eventually by Enhanced Data GSM Environment (EDGE) andUniversal Mobile Telecommunications Service (UMTS). New standards arestill being defined, but it is believed that they will have similaritiesto the network behavior described herein, and it will also be understoodby persons skilled in the art that the embodiments described herein areintended to use any other suitable standards that are developed in thefuture. The wireless link connecting the communication subsystem 104with the wireless network 200 represents one or more different RadioFrequency (RF) channels, operating according to defined protocolsspecified for GSM/GPRS communications. With newer network protocols,these channels are capable of supporting both circuit switched voicecommunications and packet switched data communications.

Although the wireless network 200 associated with mobile device 100 is aGSM/GPRS wireless network in one exemplary implementation, otherwireless networks may also be associated with the mobile device 100 invariant implementations. The different types of wireless networks thatmay be employed include, for example, data-centric wireless networks,voice-centric wireless networks, and dual-mode networks that can supportboth voice and data communications over the same physical base stations.Combined dual-mode networks include, but are not limited to, CodeDivision Multiple Access (CDMA) or CDMA2000 networks, GSM/GPRS networks(as mentioned above), and future third-generation (3G) networks likeEDGE and UMTS. Some other examples of data-centric networks include WiFi802.11, Mobitex™ and DataTAC™ network communication systems. Examples ofother voice-centric data networks include Personal Communication Systems(PCS) networks like GSM and Time Division Multiple Access (TDMA)systems. The main processor 102 also interacts with additionalsubsystems such as a Random Access Memory (RAM) 186, a flash memory 108,a display 110, an auxiliary input/output (I/O) subsystem 112, a dataport 114, a keyboard 116, a speaker 118, a microphone 120, short-rangecommunications 122 and other device subsystems 124.

Some of the subsystems of the mobile device 100 performcommunication-related functions, whereas other subsystems may provide“resident” or on-device functions. By way of example, the display 110and the keyboard 116 may be used for both communication-relatedfunctions, such as entering a text message for transmission over thenetwork 200, and device-resident functions such as a calculator or tasklist. The display is used to display images which are either storedwithin the device memory, or received from the wireless network 200.

The mobile device 100 can send and receive communication signals overthe wireless network 200 after required network registration oractivation procedures have been completed. Network access is associatedwith a subscriber or user of the mobile device 100. To identify asubscriber, the mobile device 100 requires a SIM/RUIM card 126 (i.e.Subscriber Identity Module or a Removable User Identity Module) to beinserted into a SIM/RUIM interface 128 in order to communicate with anetwork. The SIM card or RUIM 126 is one type of a conventional “smartcard” that can be used to identify a subscriber of the mobile device 100and to personalize the mobile device 100, among other things. Withoutthe SIM card 126, the mobile device 100 is not fully operational forcommunication with the wireless network 200. By inserting the SIMcard/RUIM 126 into the SIM/RUIM interface 128, a subscriber can accessall subscribed services. Services may include: web browsing andmessaging such as e-mail, voice mail, Short Message Service (SMS), andMultimedia Messaging Services (MMS). More advanced services may include:point of sale, field service and sales force automation. The SIMcard/RUIM 126 includes a processor and memory for storing information.Once the SIM card/RUIM 126 is inserted into the SIM/RUIM interface 128,it is coupled to the main processor 102. In order to identify thesubscriber, the SIM card/RUIM 126 can include some user parameters suchas an International Mobile Subscriber Identity (IMSI). An advantage ofusing the SIM card/RUIM 126 is that a subscriber is not necessarilybound by any single physical mobile device. The SIM card/RUIM 126 maystore additional subscriber information for a mobile device as well,including datebook (or calendar) information and recent callinformation. Alternatively, user identification information can also beprogrammed into the flash memory 108.

The mobile device 100 is a battery-powered device and includes a batteryinterface 132 for receiving one or more rechargeable batteries 130. Inat least some embodiments, the battery 130 can be a smart battery withan embedded microprocessor. The battery interface 132 is coupled to aregulator (not shown), which assists the battery 130 in providing powerV+ to the mobile device 100. Although current technology makes use of abattery, future technologies such as micro fuel cells may provide thepower to the mobile device 100.

The mobile device 100 also includes an operating system 134 and softwarecomponents 136 to 148 which are described in more detail below. Theoperating system 134 and the software components 136 to 148 that areexecuted by the main processor 102 are typically stored in a persistentstore such as the flash memory 108, which may alternatively be aread-only memory (ROM) or similar storage element (not shown). Thoseskilled in the art will appreciate that portions of the operating system134 and the software components 136 to 148, such as specific deviceapplications, or parts thereof, may be temporarily loaded into avolatile store such as the RAM 106. Other software components can alsobe included, as is well known to those skilled in the art.

The subset of software applications 136 that control basic deviceoperations, including data and voice communication applications, willnormally be installed on the mobile device 100 during its manufacture.Other software applications include a message application 138 that canbe any suitable software program that allows a user of the mobiledevice, 100 to send and receive electronic messages. Variousalternatives exist for the message application 138 as is well known tothose skilled in the art. Messages that have been sent or received bythe user are typically stored in the flash memory 108 of the mobiledevice 100 or some other suitable storage element in the mobile device100. In at least some embodiments, some of the sent and receivedmessages may be stored remotely from the device 100 such as in a datastore of an associated host system that the mobile device 100communicates with.

The software applications can further include a device state module 140,a Personal Information Manager (PIM) 142, and other suitable modules(not shown). The device state module 140 provides persistence, i.e. thedevice state module 140 ensures that important device data is stored inpersistent memory, such as the flash memory 108, so that the data is notlost when the mobile device 100 is turned off or loses power.

The PIM 142 includes functionality for organizing and managing dataitems of interest to the user, such as, but not limited to, e-mail,contacts, calendar events, voice mails, appointments, and task items. APIM application has the ability to send and receive data items via thewireless network 200. PIM data items may be seamlessly integrated,synchronized, and updated via the wireless network 200 with the mobiledevice subscriber's corresponding data items stored and/or associatedwith a host computer system. This functionality creates a mirrored hostcomputer on the mobile device 100 with respect to such items. This canbe particularly advantageous when the host computer system is the mobiledevice subscriber's office computer system.

The mobile device 100 also includes a connect module 144, and aninformation technology (IT) policy module 146. The connect module 144implements the communication protocols that are required for the mobiledevice 100 to communicate with the wireless infrastructure and any hostsystem, such as an enterprise system, that the mobile device 100 isauthorized to interface with. Examples of a wireless infrastructure andan enterprise system are given in FIGS. 3 and 4, which are described inmore detail below.

The connect module 144 includes a set of APIs that can be integratedwith the mobile device 100 to allow the mobile device 100 to use anynumber of services associated with the enterprise system. The connectmodule 144 allows the mobile device 100 to establish an end-to-endsecure, authenticated communication pipe with the host system. A subsetof applications for which access is provided by the connect module 144can be used to pass IT policy commands from the host system to themobile device 100. This can be done in a wireless or wired manner. Theseinstructions can then be passed to the IT policy module 146 to modifythe configuration of the device 100. Alternatively, in some cases, theIT policy update can also be done over a wired connection.

Other types of software applications can also be installed on the mobiledevice 100. These software applications can be third party applications,which are added after the manufacture of the mobile device 100. Examplesof third party applications include games, calculators, utilities, etc.

The additional applications can be loaded onto the mobile device 100through at least one of the wireless network 200, the auxiliary I/Osubsystem 112, the data port 114, the short-range communicationssubsystem 122, or any other suitable device subsystem 124. Thisflexibility in application installation increases the functionality ofthe mobile device 100 and may provide enhanced on-device functions,communication-related functions, or both. For example, securecommunication applications may enable electronic commerce functions andother such financial transactions to be performed using the mobiledevice 100.

The data port 114 enables a subscriber to set preferences through anexternal device or software application and extends the capabilities ofthe mobile device 100 by providing for information or software downloadsto the mobile device 100 other than through a wireless communicationnetwork. The alternate download path may, for example, be used to loadan encryption key onto the mobile device 100 through a direct and thusreliable and trusted connection to provide secure device communication.

The data port 114 can be any suitable port that enables datacommunication between the mobile device 100 and another computingdevice. The data port 114 can be a serial or a parallel port. In someinstances, the data port 114 can be a USB port that includes data linesfor data transfer and a supply line that can provide a charging currentto charge the battery 130 of the mobile device 100.

The short-range communications subsystem 122 provides for communicationbetween the mobile device 100 and different systems or devices, withoutthe use of the wireless network 200. For example, the subsystem 122 mayinclude an infrared device and associated circuits and components forshort-range communication. Examples of short-range communicationstandards include standards developed by the Infrared Data Association(IrDA), Bluetooth, and the 802.11 family of standards developed by IEEE.

In use, a received signal such as a text message, an e-mail message, orweb page download will be processed by the communication subsystem 104and input to the main processor 102. The main processor 102 will thenprocess the received signal for output to the display 110 oralternatively to the auxiliary I/O subsystem 112. A subscriber may alsocompose data items, such as e-mail messages, for example, using thekeyboard 116 in conjunction with the display 110 and possibly theauxiliary I/O subsystem 112. The auxiliary subsystem 112 may includedevices such as: a touch screen, mouse, track ball, infrared fingerprintdetector, or a roller wheel with dynamic button pressing capability. Thekeyboard 116 is preferably an alphanumeric keyboard and/ortelephone-type keypad. However, other types of keyboards may also beused. A composed item may be transmitted over the wireless network 200through the communication subsystem 104.

For voice communications, the overall operation of the mobile device 100is substantially similar, except that the received signals are output tothe speaker 118, and signals for transmission are generated by themicrophone 120. Alternative voice or audio I/O subsystems, such as avoice message recording subsystem, can also be implemented on the mobiledevice 100. Although voice or audio signal output is accomplishedprimarily through the speaker 118, the display 110 can also be used toprovide additional information such as the identity of a calling party,duration of a voice call, or other voice call related information.

The image module 148 implements the methods described herein fordisplaying a reduced size image on the display 110.

In the preceding description, for purposes of explanation, numerousdetails are set forth in order to provide a thorough understanding ofthe embodiments of the invention. However, it will be apparent to oneskilled in the art that these specific details are not required in orderto practice the invention. In other instances, well-known electricalstructures and circuits are shown in block diagram form in order not toobscure the invention. For example, specific details are not provided asto whether the embodiments of the invention described herein areimplemented as a software routine, hardware circuit, firmware, or acombination thereof.

Embodiments of the invention can be represented as a software productstored in a machine-readable medium (also referred to as acomputer-readable medium, a processor-readable medium, or a computerusable medium having a computer-readable program code embodied therein).The machine-readable medium can be any suitable tangible medium,including magnetic, optical, or electrical storage medium including adiskette, compact disk read only memory (CD-ROM), memory device(volatile or non-volatile), or similar storage mechanism. Themachine-readable medium can contain various sets of instructions, codesequences, configuration information, or other data, which, whenexecuted, cause a processor to perform steps in a method according to anembodiment of the invention. Those of ordinary skill in the art willappreciate that other instructions and operations necessary to implementthe described invention can also be stored on the machine-readablemedium. Software running from the machine-readable medium can interfacewith circuitry to perform the described tasks.

The above-described embodiments of the invention are intended to beexamples only. Alterations, modifications and variations can be effectedto the particular embodiments by those of skill in the art withoutdeparting from the scope of the invention, which is defined solely bythe claims appended hereto.

What is claimed is:
 1. A computer-implemented method of decoding part ofan encoded image to produce a downsampled image in the spatial domain,the downsampled image comprising reduced blocks, the encoded imagereceived as a compressed image, the method comprising: entropy decoding,by a processor, the compressed image to generate index blocks ofquantized transform domain coefficients; discarding at least one row andcolumn of each index block to generate a part of an image made up ofreduced index blocks in the transform domain; dequantizing, by theprocessor, the reduced index blocks by multiplying each block by aquantization-step-size block to generate the part of an image made up ofcoefficient blocks in the transform domain; and performing a compositeinverse-transform and mean-filtering operation on the coefficient blocksto produce reduced blocks in the spatial domain, the reduced blockscomprising downscaled spatial coefficients.
 2. The method of claim 1,wherein the entropy decoding comprises Huffman decoding, wherein theinverse transforming comprises the inverse of a Discrete CosineTransform (DCT), and wherein the transform domain is the DCT domain. 3.The method of claim 1, wherein the discarded at least one row and columncorrespond to rows and columns that are not used in the compositeinverse-transform and mean-filtering operation.
 4. The method of claim1, wherein the composite inverse-transform and mean-filtering operationcomprises a single matrix multiplication.
 5. The method of claim 1,wherein the dequantization and the composite inverse-transform andmean-filtering operation are performed by a single matrixmultiplication.
 6. The method of claim 1, wherein the mean filteringaverages pixel-values in a pixel-value block in the spatial domain. 7.The method of claim 1, wherein dequantizing the reduced index blockscomprises utilizing a quantization-step-size block of the same size asthe reduced index blocks.
 8. The method of claim 7, wherein dequantizingthe reduced index blocks further comprises decreasing the size of thequantization-step-size block to the same size as the reduced indexblocks.
 9. The method of claim 1, wherein the inverse transformcomprises performing an AAN inverse DCT transform.
 10. The method ofclaim 1, wherein the part of an encoded image comprises an entire image.11. The method of claim 2, wherein each index block is originally ofsize 8×8, wherein discarding at least one row and column comprisesdiscarding rows and columns 2, 4, and 6, and wherein the reduced blocksare of size 2×2.
 12. The method of claim 2, wherein entropy decodingcomprises computing only the DCT indices that are not discarded.
 13. Themethod of claim 12, wherein Huffman decoding comprises performingamplitude determination for the DCT indices which will not be discarded.14. The method of claim 2, wherein the coefficient blocks are 8×8matrices, and wherein the composite inverse-transform and mean-filteringoperation comprises computing:y ₀ =f ₀+(f ₁ −f ₇)×2.6132593+(f ₅ −f ₃)×1.0823922andy ₁ =f ₀−(f ₁ −f ₇)×2.6132593−(f ₅ −f ₃)×1.0823922 for each row and foreach column, wherein y₀ and y₁ are downsampled coefficients, and whereinthe f_(i) are DCT coefficients.
 15. A computer program product, embodiedon a tangible computer-readable medium, comprising instructions, whichwhen executed by a processor of a device, cause said device to decodepart of an encoded image to produce a downsampled image in a spatialdomain, the downsampled image comprising reduced blocks, the encodedimage received as a compressed image, said instructions comprisinginstructions for: entropy decoding, by a processor, the compressed imageto generate index blocks of quantized transform domain coefficients;discarding at least one row and column of each index block to generate apart of an image made up of reduced index blocks in the transformdomain; dequantizing, by the processor, the reduced index blocks bymultiplying each block by a quantization-step-size block to generate thepart of an image made up of coefficient blocks in the transform domain;and performing a composite inverse-transform and mean-filteringoperation on the coefficient blocks to produce reduced blocks in thespatial domain, the reduced blocks comprising downscaled spatialcoefficients.
 16. The computer program product of claim 15, wherein theentropy decoding operation is Huffman decoding and wherein the inversetransforming comprises the inverse of a Discrete Cosine Transform (DCT),and the transform domain is the DCT domain.
 17. The computer programproduct of claim 15, wherein the coefficient blocks are 8×8 matrices,and wherein the composite inverse-transform and mean-filtering operationcomprises computing:y ₀ =f ₀+(f ₁ −f ₇)×2.6132593+(f ₅ −f ₃)×1.0823922andy ₁ =f ₀−(f ₁ −f ₇)×2.6132593−(f ₅ −f ₃)×1.0823922 for each row and foreach column, wherein y₀ and y₁ are downsampled coefficients, and whereinthe f_(i) are DCT coefficients.
 18. The computer program product ofclaim 16, wherein each index block is originally of size 8×8, whereindiscarding at least one row and column comprises discarding rows andcolumns 2, 4, and 6, and wherein the reduced pixel-value block is ofsize 2×2.
 19. The computer program product of claim 15, wherein thedequantization and the composite inverse-transform and mean-filteringoperation are performed by a single matrix multiplication
 20. A devicefor decoding and displaying an encoded image to produce a downsampledimage in a spatial domain, the downsampled image comprising reducedblocks, the encoded image received as a compressed image, the devicecomprising: a processor configured to execute instructions embodied on acomputer-readable medium to provide: means for entropy decoding, by aprocessor, the compressed image to generate index blocks of quantizedtransform domain coefficients; means for discarding at least one row andcolumn of each index block to generate a part of an image made up ofreduced index blocks in the transform domain; means for dequantizing, bythe processor, the reduced index blocks by multiplying each block by aquantization-step-size block to generate the part of an image made up ofcoefficient blocks in the transform domain; and means for performing acomposite inverse-transform and mean-filtering operation on thecoefficient blocks to produce reduced blocks in the spatial domain, thereduced blocks comprising downscaled spatial coefficients; and a displayfor displaying the part of an image.